---
language:
- ta
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Tamil
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: ta_in
      split: test
    metrics:
    - type: wer
      value: 15.8
      name: WER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: ta
      split: test
    metrics:
    - type: wer
      value: 11.15
      name: WER
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small Ta - Bharat Ramanathan (Kudos to him for developing it)
# This is a copy of his model for academic purpose.

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1803
- Wer: 17.1456

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3374        | 0.1   | 500  | 0.2579          | 23.3804 |
| 0.29          | 0.2   | 1000 | 0.2260          | 20.9937 |
| 0.2522        | 0.3   | 1500 | 0.2139          | 20.0682 |
| 0.2338        | 0.4   | 2000 | 0.2025          | 19.6785 |
| 0.223         | 0.5   | 2500 | 0.1979          | 18.3147 |
| 0.211         | 0.6   | 3000 | 0.1927          | 17.8276 |
| 0.2032        | 0.7   | 3500 | 0.1865          | 17.3892 |
| 0.1978        | 0.8   | 4000 | 0.1839          | 17.5353 |
| 0.1972        | 0.9   | 4500 | 0.1812          | 17.0969 |
| 0.1894        | 1.0   | 5000 | 0.1803          | 17.1456 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2